Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way

Part 2 of a beginner-friendly series on backpropagation. Builds on Part 1 by introducing the chain rule as a systematic method for computing gradients in a neural network. Walks through each partial derivative step-by-step (loss w.r.t. prediction, prediction w.r.t. activation, ReLU derivative, and pre-activation w.r.t. weight), then assembles them via the chain rule to recover the same gradient derived classically in Part 1. Concludes by identifying the inefficiency of recomputing shared partial derivatives for each parameter separately, setting up Part 3's exploration of how backpropagation reuses intermediate results in a single backward pass.

Source: https://towardsdatascience.com/backpropagation-explained-for-beginners-part-2-there-has-to-be-a-better-way. 8sync News only summarizes and links out; content copyright belongs to the authors and original sources.